code: def save(self, path):
      filename = os.path.join(path, "model.pth")
      torch.save(self.powernet_model.state_dict(), filename)
  
file_path: PARL/benchmark/torch/NeurIPS2020-Learning-to-Run-a-Power-Network-Challenge/track2/rl_agent.py
imports: import os
  import numpy
  from copy import deepcopy
  from utils import process
  from powernet_model import PowerNetModel
  from grid2op.Agent import BaseAgent
  import torch
label_desc: Use image node_id 3 for calling the RLAgent obj's underlying member method code with example usage: obj.save(path) without return types